🤖 AI Summary
This study addresses the critical issue of racial bias in chest X-ray (CXR) diagnostic models, which often exploit implicit racial cues embedded in images, thereby compromising healthcare equity. The authors systematically evaluate the impact of various image preprocessing techniques—including lung masking, lung cropping based on bounding boxes, and Contrast-Limited Adaptive Histogram Equalization (CLAHE)—on mitigating race-encoded information in CXRs. Their findings demonstrate that bounding box–based lung cropping substantially reduces the model’s reliance on racial shortcuts while preserving high diagnostic accuracy for pulmonary diseases. This approach effectively circumvents the conventional trade-off between fairness and performance, offering a promising pathway toward developing more reliable and equitable AI systems in medical imaging.
📝 Abstract
Deep learning models can identify racial identity with high accuracy from chest X-ray (CXR) recordings. Thus, there is widespread concern about the potential for racial shortcut learning, where a model inadvertently learns to systematically bias its diagnostic predictions as a function of racial identity. Such racial biases threaten healthcare equity and model reliability, as models may systematically misdiagnose certain demographic groups. Since racial shortcuts are diffuse - non-localized and distributed throughout the whole CXR recording - image preprocessing methods may influence racial shortcut learning, yet the potential of such methods for reducing biases remains underexplored. Here, we investigate the effects of image preprocessing methods including lung masking, lung cropping, and Contrast Limited Adaptive Histogram Equalization (CLAHE). These approaches aim to suppress spurious cues encoding racial information while preserving diagnostic accuracy. Our experiments reveal that simple bounding box-based lung cropping can be an effective strategy for reducing racial shortcut learning while maintaining diagnostic model performance, bypassing frequently postulated fairness-accuracy trade-offs.